Agents in swarms , generally exhibit motion which
depends on two kinds of information in the decision process . One
is their own experience : the cd idea about how other agents
around them have performed . They must know , what has been the
most favourable position , amongst the positions explored by
thehoices which they have tried , and the location where they
were in a most favourable state themselves . The second is the
experience of other people : they hae an other agents around it .
The idea of swarm behaviour is of interest , both in the fields
of Artificial Intelligence , as well as Optimization .

General Method
for Particle Swarm Optimization ( PSO )

Kennedy and Eberhart developed a PSO concept ,
imitating the bird flocking technique in two dimentional space .
The idea can also be extended to an n - dimensional space . The
position of each agent is given by his X , Y coordinates . His
velocity is expressed in tems of vX ( velocity in X direction )
and vY ( velocity in Y direction ) . The method is aimed at
optimizing a certain function . Each agent knows the best value
it has personally attained so far ( pbest ) , as well as the
globally best value , attained amongst the other agents ( gbest )
. Besides , it also knows the coordingates associated with these
two points .

In each itereation , each agent tries to modify
itself using the following information :its current positions ( x
, y ) , its current velocity (vx,vy) , the distance betweer pbest
and its current position ,the distance between gbest and its
current position .